CN112687001B - Three-dimensional geological structure model random generation and uncertainty analysis method - Google Patents
Three-dimensional geological structure model random generation and uncertainty analysis method Download PDFInfo
- Publication number
- CN112687001B CN112687001B CN202110276990.1A CN202110276990A CN112687001B CN 112687001 B CN112687001 B CN 112687001B CN 202110276990 A CN202110276990 A CN 202110276990A CN 112687001 B CN112687001 B CN 112687001B
- Authority
- CN
- China
- Prior art keywords
- geological
- dimensional
- model
- geological structure
- random
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Abstract
The invention relates to the field of geological informatization, and discloses a three-dimensional geological structure model random generation and uncertainty analysis method, which solves the problems that three-dimensional morphological characteristics cannot be fully reflected and a single model cannot perfectly reflect the real face of an underground space in the uncertainty analysis scheme of the traditional three-dimensional geological modeling. According to the method, after the geological model is built according to geological survey data, a random model which accords with the pre-specified error distribution characteristics is generated based on Monte Carlo simulation, dimension reduction processing is carried out on the generated model based on a multi-dimensional scale analysis method, all the models are mapped into point sets in a three-dimensional space, and comparison analysis and visualization analysis of model differences are achieved through the relative position relation among the point sets. The method is suitable for uncertainty analysis of the three-dimensional geological model.
Description
Technical Field
The invention relates to the field of geological informatization, in particular to a three-dimensional geological structure model random generation and uncertainty analysis method.
Background
Because the geological survey data has inherent errors in the measurement and interpretation processes, an interpolation algorithm adopted by the three-dimensional geological modeling has randomness, and in addition, the underground structure is complex and diverse and is difficult to predict, modeling personnel can possibly misjudge the structural form of the model, so that the problem of uncertainty inevitably exists in the modeling process.
The prior art for uncertainty analysis is generally based on a best estimation model, and uncertainty is quantitatively characterized by scalar values such as probability values and fuzzy values. Since scalars are zero-dimensional and the geologic structure model is three-dimensional in nature, the morphological features of the three-dimensional model cannot be fully reflected with zero-dimensional scalars, and in addition, relying on only a single geologic model is not sufficient to perfectly reflect the true face of the subsurface space.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a Monte Carlo simulation-based three-dimensional geological structure model random generation and uncertainty analysis method is provided, and the problems that three-dimensional morphological characteristics cannot be fully reflected and a single model cannot perfectly reflect the real face of an underground space in a traditional uncertainty analysis scheme for three-dimensional geological modeling are solved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the three-dimensional geological structure model random generation and uncertainty analysis method comprises the following steps:
a. constructing a three-dimensional geological structure model according to the existing geological exploration data;
b. the error distribution characteristics of the elevation values of the nodes of the structural surface are appointed, and error distribution parameters under different disturbance intensities are obtained through calculation;
c. carrying out Monte Carlo random simulation on the elevation values of all structural surface nodes of the constructed three-dimensional geological structure model based on the error distribution parameters under different disturbance intensities to obtain a corresponding number of randomly simulated three-dimensional geological structure models;
d. carrying out model reconstruction on the three-dimensional geological structure models with corresponding number which are simulated at random;
e. calculating a normal vector and a Gaussian curvature at a node of the reconstructed three-dimensional geological structure model;
f. and performing dimensionality reduction analysis on all the reconstructed three-dimensional geological structure models by adopting a multi-dimensional scale analysis method based on the normal vector and the Gaussian curvature respectively.
As a further optimization, in the step a, according to the existing geological exploration data, a three-dimensional geological structure model is constructed by adopting three-dimensional geological modeling software SKUA-GOCAD, and the model is an optimal estimation model; the existing geological exploration data comprises terrain contour lines, geological mapping data, drilling data, excavation data, geological profile maps and the like.
As a further optimization, in the step b, normal distribution is used as an error distribution characteristic of the elevation value of the structural plane node, and the calculating to obtain error distribution parameters under different disturbance intensities specifically includes:
calculating standard deviations corresponding to the elevation values of the nodes at different positions:
wherein the content of the first and second substances,
the standard deviation corresponding to the elevation value of the node i;
is a distance factor for the node i and,
the distance between node i and its nearest borehole,
for all in the current structural plane
The maximum value of (a) is,
in order to be a global perturbation factor,
a priori sampling standard deviation for the borehole;
by selecting different
And
the standard deviation under different disturbance intensities can be obtained
。
As a further optimization, step c specifically includes:
c1, obtaining the elevation value of the node i of the constructed three-dimensional geological structure modelZ i ；
c2, obtaining random numbers which are subject to standard normal distribution through Box-Muller algorithm
：
Firstly, two random numbers which are subject to uniform distribution are obtained
Then by the formula
c3 calculating the simulated height value
：
c4 based onqStandard deviation of the signals under different disturbance intensities
And repeating the steps c2-c3 to carry out all the nodes on the constructed three-dimensional geological structure modelySub-simulation to obtain a simulated elevation value
Replacing elevation values on corresponding nodesZ i Then can obtainq*yAnd (3) randomly simulating a three-dimensional geological structure model.
As a further optimization, step d specifically includes:
d1, geological interface smoothing:
firstly, setting vertical constraint for geological interface boundaries of all three-dimensional geological structure models obtained through simulation, then setting elevation points obtained through simulation as fixed constraint points, then encrypting a triangular net of the geological interface, and then re-interpolating the geological interface by utilizing a discrete smooth interpolation technology, so that the geological interface is smooth under the condition of fitting the simulated elevation points;
d2, model topology correction:
firstly, setting a distance constraint for geological interfaces, and then carrying out interpolation again so as to eliminate the situation of mutual interpenetration; and (3) aiming at the condition that the junction of the geological grids is not jointed, restraining the geological interface boundary with wrong topology at the junction to a target geological interface by utilizing a curved surface boundary-curved surface constraint technology, and then carrying out interpolation so as to realize the perfect jointing of the geological interface boundary.
As a further optimization, step f specifically includes:
f1, obtaining a distance matrix Dist based on the normal vector and the Gaussian curvature calculation result;
f2, constructing a matrix T, wherein each element in T
And each element in the distance matrix Dist
The relationship of (1) is:
wherein the content of the first and second substances,
is the ith row and the jth column element in the matrix T;
is the ith row and the jth column element in the distance matrix Dist; n is the row number of the distance matrix Dist and also represents the number of the random models;
f3, performing eigenvalue decomposition on the matrix T, then sequencing all eigenvalues, and constructing a diagonal matrix by using the maximum three eigenvalues
Constructing a matrix U by using the three rows of eigenvectors corresponding to the three rows of eigenvectors;
f4, using diagonal matrix
And calculating a final dimensionality reduction result X by the matrix U:
x is an n-3 matrix, and n represents the number of the random models;
f5, taking 3 elements of each row in the dimension reduction result X as coordinates of the midpoint of the three-dimensional space, and presenting the coordinates in the three-dimensional space;
f6, and carrying out contrast analysis and visualization analysis of model difference through the relative position relationship between points in the three-dimensional space.
The invention has the beneficial effects that:
after the geological model is built according to geological survey data, a random model which accords with the pre-specified error distribution characteristics is generated based on Monte Carlo simulation, dimension reduction processing is carried out on the generated model based on a multi-dimensional scale analysis method, all the models are mapped into point sets in a three-dimensional space, and comparison analysis and visualization analysis of model differences are realized through the relative position relationship among the point sets;
the constructed three-dimensional geological model is disturbed and randomly simulated to different degrees, so that a random structural surface model with comprehensiveness is obtained, and the differences of all random models are visually reflected through uncertainty representation and visualization analysis of a three-dimensional normal vector and Gaussian curvature, so that the accurate model can be selected to more truly reflect the face of the underground space, and scientific guidance is provided for geological exploration.
Drawings
FIG. 1 is a flow chart of a method for randomly generating a three-dimensional geological structure model and analyzing uncertainty in the invention.
Detailed Description
The invention aims to provide a Monte Carlo simulation-based three-dimensional geological structure model random generation and uncertainty analysis method, and solves the problems that three-dimensional morphological characteristics cannot be fully reflected and a single model cannot perfectly reflect the real face of an underground space in the uncertainty analysis scheme of the traditional three-dimensional geological modeling.
Considering that a three-dimensional normal vector and a Gaussian curvature on a geological structure surface are closely related to a model form, the method brings the normal vector and the Gaussian curvature on the geological structure surface into uncertainty analysis so as to break through the traditional uncertainty analysis idea only based on a zero-dimensional scalar; in the modeling process, the best estimation model is not directly adopted for uncertainty analysis, but the constructed best estimation model is subjected to disturbance and random simulation in different degrees through a reasonable scheme to obtain a whole set of random structure surface model, and then the generated random model is subjected to comparative analysis, so that the geological model which is more in line with the real situation of the underground space is selected to guide production.
In particular implementation, the scheme flow of the invention is shown in fig. 1, and the scheme flow comprises the following implementation steps:
1. constructing a three-dimensional geological structure model:
in the step, according to the existing geological exploration data (such as terrain contour lines, geological profile data, drilling data, excavation data, geological profile maps and the like); the construction of a three-dimensional geological structure model is completed by utilizing the acknowledged mainstream geological modeling software SKUA-GOCAD in the current three-dimensional geological modeling field, and the geological structure models mentioned in the invention all belong to the Surface data format in SKUA-GOCAD and are irregular triangular meshes in nature. As for the specific process of realizing model construction by modeling software, the method belongs to the prior art and is not described herein again.
2. Specifying error distribution characteristics, and calculating error distribution parameters:
in the step, the error distribution characteristics of the elevation values of the nodes of the structural plane are specified, and error distribution parameters under different disturbance intensities are obtained through calculation.
According to the relevant research of geological modeling uncertainty, the error distribution of the node elevation value in the geological structural plane can be generally regarded as obeying normal distribution or even distribution, and the like, and the following two conditions should be satisfied theoretically:
(1) the error range of the elevation value of the node generally depends on the distance between the node and the nearest drilling hole of the node, namely, the error range of the node is smaller when the node is closer to the nearest drilling hole of the node, and the error range is larger when the node is farther away.
(2) The increasing trend of the error range should exhibit a gradually increasing non-linear increasing trend as the distance between the node and its nearest borehole increases.
Considering that a simple and reasonable parameter setting method is not available at present, based on the above criteria, the invention takes normal distribution as an error distribution characteristic and designs a formula for assigning values to error distribution parameters of node elevation values at different positions:
in the formula, i represents the node serial number on the triangular grid of the geological surface;
the distance between the node with sequence number i and the nearest borehole,
representing all of the current structural plane
Maximum value of (d); with the current node
Maximum with global
Is taken as the distance factor of the current node i
；
On the basis, the user can set different global disturbance factors according to actual requirements
And prior standard deviation of sampling at different boreholes
Further calculate the standard deviation corresponding to the elevation value of the node i
。
The formula can better satisfy the two criteria, and the user can select different criteria
And
obtaining standard deviation under different disturbance intensities
，
The larger, i.e. generationThe larger the table error range.
3. Obtaining a randomly simulated three-dimensional geological structure model through Monte Carlo random simulation:
in the step, Monte Carlo random simulation is carried out on the elevation values of all structural surface nodes of the constructed three-dimensional geological structure model based on the error distribution parameters under different disturbance intensities, and the three-dimensional geological structure models with corresponding quantity are obtained through random simulation;
taking node i as an example for explanation, the elevation value of node i of the three-dimensional geological structure model is assumed to beZ i Then based on the standard deviation
Simulated elevation value of
The method is characterized in that codes are compiled based on secondary development of SKUA-GOCAD, and the simulation process comprises the following steps:
(1) obtaining the elevation value of the node iZ i ；
(2) Obtaining random numbers obeying standard normal distribution by Box-Muller algorithm
: firstly, utilizing C + + standard library function rand () to obtain two random numbers obeying uniform distribution
Then by the formula
(3) Computing
: due to the simulation of the height value
Thus, therefore, it is
。
In the above process, obtain one
Namely, the random simulation aiming at the node i is finished once, and the random simulation is developed secondarily through SKUA-GOCAD and is respectively based onq（q≧ 2) different disturbance intensities (e.g.: low strength, medium strength, high strength) standard deviation
Calculating results, and respectively performing the above method on all nodes on the surface model of the geological structurey（yMore than or equal to 1) times of simulation, and then adjusting the elevation value of the node to be corresponding
Can obtainqSets of stochastic geological models, each setyAnd (4) respectively. The number of simulations can be specified according to user requirements. In addition, the values of q and y depend on hardware level, data size, calculation time acceptability, precision requirements of users on uncertainty analysis results and other factors, and are determined by the users according to the requirements of the users, such as: the requirement on the precision of the result is higher, the calculation time and the data size can be accepted, and the hardware level is also supported, so that the calculation time and the data size can be set to be slightly larger, and more three-dimensional geological structure models can be obtained and simulated; conversely, a relatively small one may be provided.
4. Reconstructing the simulated three-dimensional geological structure model:
in the step, model reconstruction is carried out on the three-dimensional geological structure models with the corresponding number which are simulated at random.
The geological structure model obtained by random simulation may have the following two problems:
(1) because the elevation values of the grid nodes in the model are randomly generated, the situation that local unevenness and smoothness are insufficient can occur;
(2) due to the change of the geological grid form in the model, topological errors such as mutual interpenetration of geological interfaces or mismatch of boundaries of geological curved surfaces can occur.
In view of the above problems, the reconstructed structural surface model mainly includes two aspects:
(1) and (3) geological interface smoothing treatment:
based on secondary development of SKUA-GOCAD, vertical constraints are firstly set on all geological interface boundaries obtained through simulation, then elevation points obtained through simulation are set as fixed constraint points, on the basis, the triangular net is encrypted through secondary development of SKUA-GOCAD, then interpolation is carried out on the geological interface again through the discrete smooth interpolation technology, and therefore the geological interface is smooth and reasonable under the condition that the simulated elevation points are attached.
(2) And (3) model topology modification treatment:
based on secondary development of SKUA-GOCAD, a smaller distance constraint (such as 0.01 m) is firstly set between geological interfaces to ensure that a minimum distance is kept between structural planes, and then interpolation is carried out again to eliminate the situation of mutual interpenetration; aiming at the problem that the junction of the geological grid is not jointed, the geological interface boundary with wrong topology at the junction is constrained to a target geological interface by using a built-in 'curved surface boundary-curved surface' constraint technology of SKUA-GOCAD, and then interpolation is carried out, so that the junction of the geological interface is perfectly jointed, and the topological correction of the geological interface is completed.
5. Calculating normal vectors and gaussian curvatures at nodes:
in the step, normal vectors and Gaussian curvatures at nodes of the reconstructed three-dimensional geological structure model are calculated.
After a plurality of random geological models are obtained, normal vectors and Gaussian curvatures at nodes of the curved surface grid can be calculated in sequence based on SKUA-GOCAD secondary development, and the average value of the normal vectors and the Gaussian curvatures is calculated. The calculation steps are as follows:
(1) normal vector:
taking a certain node p as an example, all triangles in k neighborhood are
Based on secondary development of SKUA-GOCAD, a normal () function in a Trgl3d class and a normal () function in a Vector3d class are called, and a unit normal Vector of each triangle is obtained through calculation in sequence
The unit normal vector N at the p node is:
wherein the weight value
Is the centroid point of the triangle in the neighborhood. The normal vectors at all nodes on the triangular mesh can be obtained by the weighted averaging method.
(2) Gaussian curvature:
taking a certain node p as an example, the unit normal vector at the node p is N, and the nodes in k neighborhood thereof
The corresponding unit normal vectors are respectively
. Will vector
Projected onto the tangent plane at point p, then along the tangent
，
The normal curvature at point p is:
maximum of the m normal curvatures obtained
Comprises the following steps:
t id representing the tangential direction corresponding to the maximum value in the m normal curvatures;
then establishing a coordinate system on a tangent plane where the point p is positioned
：
Then tangent vector
And coordinate axis
Counter clockwise angle of
The sine and cosine values of (a) may be obtained as follows:
on the basis, the Gaussian curvature K at the node pGThe final calculation method of (2) is as follows:
wherein, the coefficients a, b, c are respectively:
6. performing dimensionality reduction analysis on the reconstructed three-dimensional geological structure model:
in this step, a multidimensional scaling analysis (MDS) is used to perform dimensionality reduction analysis on all reconstructed three-dimensional geologic structure models based on the normal vectors and the Gaussian curvatures respectively. MDS dimensionality reduction analysis can be realized by writing codes based on Matlab, and the method comprises the following specific steps:
(1) based on different parameters, utilizing Matlab to embed functions
obtaining a distance matrix Dist thereof, wherein: y represents an original data matrix to be calculated; DISTANCE represents the type of DISTANCE selected, here the euclidean DISTANCE ('euclidean'); d represents the output relative distance result, and is a row vector; dist represents the distance matrix converted from the row vector D;
(2) constructing a matrix T, each element in T
And each element in the distance matrix Dist
The relationship of (1) is:
(3) after T is obtained, built-in function is utilized
The eigenvalue decomposition is carried out on the matrix T, and since the dimensionality reduction result represented by the maximum eigenvalue has the highest similarity with the initial distance, only the maximum eigenvalue needs to be selected finallySeveral eigenvalues and their corresponding eigenvectors are used to obtain the dimension reduction result. In specific operation, all the characteristic values are sequenced, and the maximum three characteristic values are utilized to construct a diagonal matrix
Constructing a matrix U by using the three rows of eigenvectors corresponding to the three rows of eigenvectors;
(4) by using
And U, calculating a final dimension reduction result X:
(5) because 3 characteristic values are selected, the final dimensionality reduction result X is an n-3 matrix, n represents the number of random models, 3 elements of each row in X can be used as coordinates of a midpoint in a three-dimensional space, and a Matlab built-in function is utilized
The method comprises the steps of respectively carrying out dimension reduction treatment on a plurality of sets of random geological models obtained under different disturbance intensities based on normal vectors and Gaussian curvatures, endowing point sets obtained through the dimension reduction treatment on the random models under different disturbance intensities with different colors, and on the basis, realizing uncertainty representation and visual analysis of the geological models by means of relative distances among points in dimension reduction results, distribution characteristics of the point sets, spatial position differences and the like.
Claims (5)
1. The three-dimensional geological structure model random generation and uncertainty analysis method is characterized by comprising the following steps of:
a. constructing a three-dimensional geological structure model according to the existing geological exploration data;
b. the error distribution characteristics of the elevation values of the nodes of the structural surface are appointed, and error distribution parameters under different disturbance intensities are obtained through calculation;
c. carrying out Monte Carlo random simulation on the elevation values of all structural surface nodes of the constructed three-dimensional geological structure model based on the error distribution parameters under different disturbance intensities to obtain a corresponding number of randomly simulated three-dimensional geological structure models;
d. carrying out model reconstruction on the three-dimensional geological structure models with corresponding number which are simulated at random;
e. calculating a normal vector and a Gaussian curvature at a node of the reconstructed three-dimensional geological structure model;
f. performing dimensionality reduction analysis on all reconstructed three-dimensional geological structure models by adopting a multi-dimensional scale analysis method based on the normal vector and the Gaussian curvature respectively;
the step f specifically comprises the following steps:
f1, obtaining a distance matrix Dist based on the normal vector and the Gaussian curvature calculation result;
f2, constructing a matrix T, wherein each element in T
And each element in the distance matrix Dist
The relationship of (1) is:
wherein the content of the first and second substances,
is the ith row and the jth column element in the matrix T;
is the ith row and the jth column element in the distance matrix Dist; n is the row number of the distance matrix Dist and also represents the number of the random models;
f3, performing eigenvalue decomposition on the matrix T, then sequencing all eigenvalues, and constructing a diagonal matrix by using the maximum three eigenvalues
Constructing a matrix U by using the three rows of eigenvectors corresponding to the three rows of eigenvectors;
f4, using diagonal matrix
And calculating a final dimensionality reduction result X by the matrix U:
wherein X is an n-3 matrix, and n represents the number of the random models;
f5, taking 3 elements of each row in the dimension reduction result X as coordinates of the midpoint of the three-dimensional space, and presenting the coordinates in the three-dimensional space;
f6, and carrying out contrast analysis and visualization analysis of model difference through the relative position relationship between points in the three-dimensional space.
2. The method of random generation and uncertainty analysis of three-dimensional geological structure models according to claim 1,
in the step a, a three-dimensional geological structure model is constructed by adopting three-dimensional geological modeling software SKUA-GOCAD according to the existing geological exploration data; the existing geological survey data comprises: topographic contours, geological profile data, drilling data, excavation data, and geological profiles.
3. The method of random generation and uncertainty analysis of three-dimensional geological structure models according to claim 1,
in the step b, the normal distribution is used as an error distribution characteristic of the elevation value of the structural plane node, and the calculating to obtain the error distribution parameters under different disturbance intensities specifically comprises:
calculating standard deviations corresponding to the elevation values of the nodes at different positions:
wherein the content of the first and second substances,
the standard deviation corresponding to the elevation value of the node i;
is a distance factor for the node i and,
the distance between node i and its nearest borehole,
for all in the current structural plane
The maximum value of (a) is,
in order to be a global perturbation factor,
a priori sampling standard deviation for the borehole;
by selecting different
And
the standard deviation under different disturbance intensities can be obtained
。
4. The method of random generation and uncertainty analysis of three-dimensional geological structure models according to claim 1,
the step c specifically comprises the following steps:
c1, obtaining the elevation value of the node i of the constructed three-dimensional geological structure modelZ i ；
c2, obtaining random numbers which are subject to standard normal distribution through Box-Muller algorithm
：
Firstly, two random numbers which are subject to uniform distribution are obtained
Then by the formula
Calculating to obtain random number
；
3. Calculating the simulated elevation value
：
c4 based onqStandard deviation of the signals under different disturbance intensities
And repeating the steps c2-c3 to carry out all the nodes on the constructed three-dimensional geological structure modelySub-simulation to obtain a simulated elevation value
Replacing elevation values on corresponding nodesZ i Then can obtainq*yAnd (3) randomly simulating a three-dimensional geological structure model.
5. The method of random generation and uncertainty analysis of three-dimensional geological structure models according to claim 1,
the step d specifically comprises the following steps:
d1, geological interface smoothing:
firstly, setting vertical constraint for geological interface boundaries of all three-dimensional geological structure models obtained through simulation, then setting elevation points obtained through simulation as fixed constraint points, then encrypting a triangular net of the geological interface, and then re-interpolating the geological interface by utilizing a discrete smooth interpolation technology, so that the geological interface is smooth under the condition of fitting the simulated elevation points;
d2, model topology correction:
firstly, setting a distance constraint for geological interfaces, and then carrying out interpolation again so as to eliminate the situation of mutual interpenetration; and (3) aiming at the condition that the junction of the geological grids is not jointed, restraining the geological interface boundary with wrong topology at the junction to a target geological interface by utilizing a curved surface boundary-curved surface constraint technology, and then carrying out interpolation so as to realize the perfect jointing of the geological interface boundary.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110276990.1A CN112687001B (en) | 2021-03-15 | 2021-03-15 | Three-dimensional geological structure model random generation and uncertainty analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110276990.1A CN112687001B (en) | 2021-03-15 | 2021-03-15 | Three-dimensional geological structure model random generation and uncertainty analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112687001A CN112687001A (en) | 2021-04-20 |
| CN112687001B true CN112687001B (en) | 2021-06-01 |
Family
ID=75455565
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110276990.1A Active CN112687001B (en) | 2021-03-15 | 2021-03-15 | Three-dimensional geological structure model random generation and uncertainty analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112687001B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023159420A1 (en) * | 2022-02-24 | 2023-08-31 | 福州大学 | Distance and direction relation uncertainty measurement method |
| CN114422107B (en) * | 2022-03-31 | 2022-06-17 | 四川高速公路建设开发集团有限公司 | Fault-tolerant ciphertext data aggregation method based on intelligent engineering construction system platform |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102074047A (en) * | 2011-01-06 | 2011-05-25 | 天津市星际空间地理信息工程有限公司 | High-fineness urban three-dimensional modeling method |
| CN102645229A (en) * | 2012-04-06 | 2012-08-22 | 东南大学 | Spaceborne laser altimeter in-orbit moon elevation detection uncertainty evaluation method |
| CN103700141A (en) * | 2013-11-19 | 2014-04-02 | 中国矿业大学(北京) | Multistage dimension geologic modeling analysis method of coal seam floor water gushing passageway |
| CN105869209A (en) * | 2016-03-29 | 2016-08-17 | 浙江大学 | Deformed triangular data processing method in three-dimensional geological surface model |
| CN110007343A (en) * | 2019-04-18 | 2019-07-12 | 中国矿业大学 | A kind of evaluation method of regional tectonics complexity |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105869210A (en) * | 2016-03-29 | 2016-08-17 | 浙江大学城市学院 | Interpolation data processing method in three-dimensional geological surface model |
| US10002407B1 (en) * | 2017-08-11 | 2018-06-19 | Intermap Technologies Inc. | Method and apparatus for enhancing 3D model resolution |
| CN108564605B (en) * | 2018-04-09 | 2020-04-07 | 大连理工大学 | Three-dimensional measurement point cloud optimization registration method |
-
2021
- 2021-03-15 CN CN202110276990.1A patent/CN112687001B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102074047A (en) * | 2011-01-06 | 2011-05-25 | 天津市星际空间地理信息工程有限公司 | High-fineness urban three-dimensional modeling method |
| CN102645229A (en) * | 2012-04-06 | 2012-08-22 | 东南大学 | Spaceborne laser altimeter in-orbit moon elevation detection uncertainty evaluation method |
| CN103700141A (en) * | 2013-11-19 | 2014-04-02 | 中国矿业大学(北京) | Multistage dimension geologic modeling analysis method of coal seam floor water gushing passageway |
| CN105869209A (en) * | 2016-03-29 | 2016-08-17 | 浙江大学 | Deformed triangular data processing method in three-dimensional geological surface model |
| CN110007343A (en) * | 2019-04-18 | 2019-07-12 | 中国矿业大学 | A kind of evaluation method of regional tectonics complexity |
Non-Patent Citations (3)
| Title |
|---|
| "3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry";James, MR 等;《EARTH SURFACE PROCESSES AND LANDFORMS》;20170930;第42卷(第12期);第1769-1788页 * |
| "一种混合几何曲率和克里金插值的平滑地质曲面构建方法";王长鹏 等;《测绘地理信息》;20200228;第45卷(第1期);第62-65页 * |
| "三维地理信息模型质检系统的设计与实现";侯亚娟 等;《城市勘探》;20151231(第6期);第41-43页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112687001A (en) | 2021-04-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10338252B1 (en) | Systems and processes for building multiple equiprobable coherent geometrical models of the subsurface | |
| Engwirda | JIGSAW-GEO (1.0): locally orthogonal staggered unstructured grid generation for general circulation modelling on the sphere | |
| EP1756517B1 (en) | System and method for approximating an editable surface | |
| RU2282242C2 (en) | Method for evening out a grid of nodes in accordance to characteristic features in digital image | |
| CN112687001B (en) | Three-dimensional geological structure model random generation and uncertainty analysis method | |
| CN109584357A (en) | Three-dimensional modeling method, device, system and storage medium based on more contour lines | |
| CN107221028B (en) | A kind of geologic body closed surface three-dimensional rebuilding method based on seismic interpretation data | |
| CN105117809A (en) | Method for optimizing well drilling sequence of offshore cluster well group | |
| Feng et al. | An adaptive sampling method for accurate measurement of aeroengine blades | |
| CN114943178A (en) | Three-dimensional geological model modeling method and device and computer equipment | |
| He et al. | eFESTA: Ensemble feature exploration with surface density estimates | |
| CN105931297A (en) | Data processing method applied to three-dimensional geological surface model | |
| CN111968227B (en) | Three-dimensional geological fault network uncertainty analysis method, system and storage medium | |
| CN113486429A (en) | Automatic construction method of space intersection structure based on interpolation algorithm | |
| CN106842314B (en) | The determination method of formation thickness | |
| CN114676644B (en) | Machine learning constraint-based density mutation interface inversion method and system | |
| CN105869209A (en) | Deformed triangular data processing method in three-dimensional geological surface model | |
| CN105785444B (en) | Velocity field construction method | |
| Wurster et al. | Neural Stream Functions | |
| Song et al. | GANSim-surrogate: An integrated framework for conditional geomodelling and uncertainty analysis | |
| Chen et al. | A study on information inconsistency between measurement and geological exploration in geological survey | |
| CN105893492A (en) | Method for processing curved-surface intersection data in three-dimensional geological surface model | |
| Xu et al. | Vector object-based architecture modeling method and condition to dense well data | |
| CN105913491A (en) | Gridding data processing method for three-dimensional geological surface model | |
| Skytt et al. | Locally refined spline surfaces for terrain and sea bed data: tailored approximation, export and analysis tools |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |